Hooshyar Danial, Pedaste Margus, Yang Yeongwook
Institute of Education, University of Tartu, Tartu 50103, Estonia.
Entropy (Basel). 2019 Dec 20;22(1):12. doi: 10.3390/e22010012.
A significant amount of research has indicated that students' procrastination tendencies are an important factor influencing the performance of students in online learning. It is, therefore, vital for educators to be aware of the presence of such behavior trends as students with lower procrastination tendencies usually achieve better than those with higher procrastination. In the present study, we propose a novel algorithm-using student's assignment submission behavior-to predict the performance of students with learning difficulties through procrastination behavior (called PPP). Unlike many existing works, PPP not only considers late or non-submissions, but also investigates students' behavioral patterns before the due date of assignments. PPP firstly builds feature vectors representing the submission behavior of students for each assignment, then applies a clustering method to the feature vectors for labelling students as a procrastinator, procrastination candidate, or non-procrastinator, and finally employs and compares several classification methods to best classify students. To evaluate the effectiveness of PPP, we use a course including 242 students from the University of Tartu in Estonia. The results reveal that PPP could successfully predict students' performance through their procrastination behaviors with an accuracy of 96%. Linear support vector machine appears to be the best classifier among others in terms of continuous features, and neural network in categorical features, where categorical features tend to perform slightly better than continuous. Finally, we found that the predictive power of all classification methods is lowered by an increment in class numbers formed by clustering.
大量研究表明，学生的拖延倾向是影响学生在线学习表现的一个重要因素。因此，教育工作者必须意识到这种行为趋势的存在，因为拖延倾向较低的学生通常比拖延倾向较高的学生表现更好。在本研究中，我们提出了一种新颖的算法——利用学生的作业提交行为——通过拖延行为来预测学习困难学生的表现（称为PPP）。与许多现有研究不同，PPP不仅考虑作业提交延迟或未提交的情况，还研究作业截止日期前学生的行为模式。PPP首先构建表示每个作业学生提交行为的特征向量，然后对特征向量应用聚类方法，将学生标记为拖延者、潜在拖延者或非拖延者，最后采用并比较几种分类方法以对学生进行最佳分类。为了评估PPP的有效性，我们使用了爱沙尼亚塔尔图大学一门包含242名学生的课程。结果显示，PPP能够通过学生的拖延行为成功预测学生表现，准确率达96%。就连续特征而言，线性支持向量机似乎是所有分类器中表现最佳的，而对于分类特征，神经网络表现最佳，其中分类特征的表现往往略优于连续特征。最后，我们发现聚类形成的类别数量增加会降低所有分类方法的预测能力。
